发表机构
The University of Texas Health Science Center at Houston; Yale School of Medicine; Texas A&M University; Stanford University School of Medicine（德克萨斯大学休斯顿健康科学中心; 耶鲁大学医学院; 德克萨斯A&M大学; 斯坦福大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出MEVA框架，融合皮层表面几何与体积影像特征进行自监督学习，在UK Biobank的GWAS中发现更多显著遗传位点，表明表面几何可捕获额外可遗传变异。
AI 中文摘要
现有的脑影像全基因组关联研究（GWAS）使用预定义或深度学习衍生的影像表型，但这些表型要么来自体积扫描，要么来自皮层表面网格，因此各自仅捕获脑解剖结构中可遗传变异的一部分。本文介绍了MEVA（网格增强体积自编码器），一种自监督框架，将体素级图像强度与皮层网格几何（包括每个表面顶点的曲率和皮层厚度）编码到一组共享的影像特征中。在MEVA中结合网格和体积输入，在年龄和性别预测方面比单独使用任一输入的模型获得了适度的性能提升。当这些特征作为UK Biobank中GWAS的表型时，它们比单独从体积或单独从网格学习的特征揭示了更多的全基因组显著位点。这些结果表明，在体积自监督学习中加入皮层表面几何可以捕获额外的可遗传变异，从而增加检测到的位点数量。
英文摘要
Existing genome-wide association studies (GWAS) of brain imaging provide predefined or deep-learning-derived imaging phenotypes, yet these phenotypes come from either volumetric scans or cortical surface meshes, so each captures only part of the heritable variation in brain anatomy. Here we introduce MEVA (Mesh-Enhanced Volumetric Autoencoder), a self-supervised framework that encodes voxel-level image intensity together with cortical mesh geometry, including curvature and cortical thickness at each surface vertex, into one shared set of imaging features. Combining the mesh and volumetric inputs in MEVA yields modest performance gains in age and sex prediction over models that use either input alone. When these features serve as phenotypes for GWAS in the UK Biobank, they reveal more genome-wide significant loci than features learned from volumes alone or from meshes alone. These results suggest that adding cortical surface geometry to volumetric self-supervised learning captures additional heritable variation and so increases the number of loci detected.
Comments17 pages, 3 figures, 1 table, 2 supplementary tables
